code: def cross_slot_reduce(self, per_slot_metrics):
      lm_losses, token_count, char_count = zip(*per_slot_metrics)
      lm_losses = [item for sublist in lm_losses for item in sublist]
  
      metrics = {"lm_loss": np.mean(lm_losses)}
      metrics["lm_loss_ppl"] = np.exp(metrics["lm_loss"])
      if self.char_level_ppl:
          tokens_per_char = sum(token_count) / sum(char_count)
          metrics["lm_loss_char_lvl_ppl"] = np.exp(
              metrics["lm_loss"] * tokens_per_char
          )
      return metrics
  
imports: import logging
  import os
  import numpy
  from attrdict import AttrMap
  from eval_tasks.eval_adapter import run_eval_harness
  from megatron.neox_arguments import NeoXArgs
  from torch.utils.tensorboard import SummaryWriter
  from determined.pytorch import MetricReducer, PyTorchCallback
label_desc: Use image node_id 5 for calling the LMReducers obj's underlying member method code with example usage: obj.cross_slot_reduce(per_slot_metrics) and returns: metrics